R：如何兩個相關組合矩陣使用GGPLOT2

我有兩個科爾矩陣，我想在1個情節相結合：對科爾矩陣1R：如何兩個相關組合矩陣使用GGPLOT2

示例代碼：

matrix_values <- c(-0.07, -0.03, 0.1, 0.11, 0.06, 0.16, 0.16, 0.13, 0.04, 0.06, 0.05, 0.04, 0.16, 0.07, 0.1, 0.08, 0.08, 0.17, 0.07, -0.13, 0.16, -0.07, 0.09, 0.07, -0.08, 0, 0.09, -0.02, 0.18, 0.09, 0.01, -0.1, -0.04, -0.12, -0.03, 0.03, 0.09, 0.09, 0.15, -0.01, 0.15, 0.09, 0.11, 0.09, 0.15, 0.19, -0.07, -0.04, 0, -0.12, NaN, -0.02, -0.11, 0.01, 0.1, -0.1, -0.1, 0.01, 0.04, 0.08, -0.02, -0.12, 0.09, -0.05, -0.07, -0.03, -0.19, -0.07, -0.16, -0.08, -0.05, -0.04, 0.03, -0.09, -0.09, -0.12, -0.07, 0.04, 0.07, 0.04, 0.02, -0.08, -0.03, -0.18, -0.02, 0.03, -0.06, 0.03, -0.07, 0.09, 0.04, -0.06, -0.1, -0.07, 0.1, 0.02, 0.06, -0.13, -0.14, -0.06, NaN, NaN, -0.07, -0.12, 0.02, -0.02, 0.01, 0.02, -0.01, -0.08, -0.03, -0.06, -0.05, -0.15, 0, -0.12, 0.13, -0.09, -0.05, 0.05, 0.08, -0.06, 0.16, 0.16, 0, 0.06, -0.05, -0.05, 0.14, -0.02, 0.12, 0.01, -0.07, -0.06, 0.07, 0.07, -0.13, 0.06, -0.05, -0.06, -0.15, -0.07, 0.11, 0.03, 0.1, 0.05, -0.12, 0.13, -0.1, 0.04, NaN, NaN, NaN, -0.03, -0.12, -0.02, 0.23, 0.13, 0.04, 0.01, 0.1, -0.01, 0.04, 0.03, -0.02, 0, -0.01, -0.08, -0.17, -0.05, 0, -0.07, -0.13, 0.1, -0.04, -0.01, 0.05, -0.03, -0.03, 0.13, -0.03, 0.01, 0.03, -0.03, 0.06, -0.01, -0.08, 0.05, 0.12, 0.09, 0.08, 0.07, -0.04, 0.09, 0.05, 0.1, 0.03, 0.05, 0.09, 0, NaN, NaN, NaN, NaN, 0.03, -0.03, 0.13, 0.14, 0.04, -0.03, 0.05, 0.14, 0.02, 0, -0.09, 0, 0, 0.01, -0.1, -0.14, 0, 0.02, 0.04, -0.07, -0.03, -0.07, -0.08, 0.1, 0.02, 0.18, 0.07, -0.16, 0.08, 0.03, -0.01, 0.03, -0.01, -0.07, 0.01, 0.1, 0.11, -0.11, 0.04, -0.08, -0.01, -0.03, -0.02, 0.09, 0.03, 0.13, NaN, NaN, NaN, NaN, NaN, -0.01, -0.05, 0.24, 0.02, 0, 0.11, 0.22, 0.22, 0.09, 0.06, 0.1, 0.09, 0.21, 0.16, 0.08, 0.08, 0.14, 0.05, 0.14, 0.15, -0.01, 0.05, 0.23, 0.13, 0.04, 0.06, 0.11, 0.05, 0.16, 0.03, 0.06, 0.01, -0.02, 0.23, -0.05, -0.09, 0.01, -0.02, 0.08, -0.07, 0.06, -0.01, -0.02, -0.03, 0.06, NaN, NaN, NaN, NaN, NaN, NaN, 0.05, -0.02, 0.08, -0.03, 0.02, -0.05, 0.13, 0.08, 0.08, 0.11, -0.04, -0.08, 0.03, 0.09, 0.1, -0.04, 0.12, 0.12, -0.06, 0.07, -0.09, 0.03, 0.03, -0.03, -0.02, 0.05, 0.04, -0.14, -0.05, 0.15, 0.06, -0.03, 0.04, -0.06, 0.21, 0.12, 0.2, -0.04, 0.05, 0.02, 0.14, 0, 0.12, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.13, 0, 0.12, 0.13, 0.05, 0.03, 0.09, 0.13, -0.05, 0.1, 0.14, 0.05, 0.06, 0.11, 0.03, 0.09, 0.17, 0.04, 0.15, 0.03, 0.03, -0.1, 0.07, 0.01, 0.02, 0.04, -0.08, 0.06, 0.05, 0.14, 0.07, 0.03, 0, 0.14, 0.02, -0.01, 0.02, 0.13, 0.09, -0.16, 0.1, -0.06, -0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.14, -0.05, 0.2, 0.05, -0.07, 0.1, 0.21, 0.14, -0.04, 0.01, 0.11, 0.1, 0.17, 0.21, 0.06, 0.09, 0.17, 0.17, 0.26, -0.04, 0.04, -0.01, 0.06, 0.14, -0.11, 0.05, 0.13, -0.05, 0.14, 0.06, 0.01, -0.05, 0.03, 0.04, 0.02, -0.08, -0.09, 0, -0.08, -0.21, -0.02, -0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.16, -0.1, 0.03, 0.06, 0.03, 0.16, 0.07, 0.09, -0.05, 0.02, 0.02, 0.02, 0.15, 0.04, 0.11, 0.04, 0.03, 0.08, 0.1, 0.06, -0.09, -0.03, 0.25, 0.11, -0.12, -0.12, 0.07, 0.03, 0.12, 0.11, 0.07, -0.07, 0.1, 0.11, -0.08, -0.05, -0.1, 0.1, -0.04, 0.07, 0.07, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.06, -0.04, 0.19, 0.04, -0.04, 0.07, 0.09, 0.07, -0.04, 0.03, 0.06, 0.1, 0.01, 0, 0.16, -0.07, 0.12, 0.07, 0.11, 0, 0.02, 0.17, 0.19, 0.13, -0.15, -0.14, 0.26, 0.08, 0.02, 0.08, 0.17, -0.03, -0.02, 0.17, 0.03, 0.03, -0.1, 0.1, -0.02, -0.2, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.02, 0.15, -0.01, -0.02, -0.19, 0, 0.05, -0.08, -0.09, -0.15, 0.16, 0.12, 0.08, -0.03, 0.11, 0.09, 0.08, 0.06, 0.11, -0.07, 0.2, 0.05, 0.22, 0.05, -0.1, -0.07, -0.08, 0.07, 0.18, -0.06, 0.12, -0.06, -0.06, 0.09, -0.12, -0.15, -0.16, 0, -0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.07, -0.1, 0.23, -0.08, 0.01, -0.02, 0.13, 0.13, -0.04, 0.14, 0.03, 0.14, 0.07, 0.15, -0.02, 0.01, 0.05, 0.03, 0, 0.15, -0.15, 0.1, 0.11, 0.17, 0, -0.06, 0.14, -0.14, 0.03, 0.16, -0.12, -0.15, -0.1, 0.17, 0.2, -0.13, -0.11, -0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.02, 0, 0.13, 0.03, -0.04, 0.03, 0.06, -0.08, -0.11, -0.08, -0.09, 0.12, 0.1, -0.01, 0.04, -0.12, -0.1, 0.01, 0.09, 0.02, 0.04, -0.03, 0.04, 0.11, -0.11, -0.15, 0.07, -0.13, -0.05, 0.15, 0.02, -0.07, 0.12, 0, 0.06, -0.05, 0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.25, -0.05, 0.29, -0.04, -0.06, 0.11, 0.16, 0.07, 0.05, 0.06, 0.12, 0.09, 0.22, 0.11, 0.17, 0.1, 0.19, 0.12, 0.17, 0.03, 0.03, 0.11, 0.19, 0.17, 0.02, 0.07, 0.27, -0.02, -0.05, 0.19, 0.16, 0, 0.11, 0.14, 0.04, 0.14, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.12, -0.08, 0.36, -0.08, 0.02, -0.03, -0.04, 0, -0.14, 0.02, -0.07, 0.05, 0.01, 0.03, -0.06, -0.03, 0.04, -0.05, 0.15, -0.03, -0.2, 0.03, 0.01, 0.1, 0.15, 0.21, 0.02, -0.2, -0.03, -0.01, -0.1, 0.02, 0.05, 0.1, -0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, 0.08, 0.2, -0.06, 0.06, 0.12, 0.2, 0.12, 0.03, 0.06, 0.08, 0.12, 0.16, 0.11, 0.15, 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-0.15, -0.22, -0.06, -0.07, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.09, -0.04, 0.02, 0.14, 0.15, 0.13, 0.02, 0.07, -0.01, 0.08, 0.1, -0.13, 0.1, -0.02, 0.02, 0.01, 0.05, 0.07, -0.07, 0.01, 0.04, -0.13, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.06, 0.14, 0.07, 0.15, 0.1, 0.09, 0.14, 0.09, 0.03, 0.04, 0.13, 0.02, 0.13, -0.02, 0.21, -0.03, 0.03, 0.12, -0.06, 0.08, 0.13, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.04, -0.09, 0.08, 0.01, 0.04, 0.01, 0, 0.06, 0.04, 0.03, 0.09, -0.12, -0.06, -0.01, -0.09, -0.11, -0.07, -0.04, -0.05, -0.1, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.1, 0.07, 0.19, -0.06, 0.12, -0.09, 0.13, 0.2, -0.16, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0, 0.06, 0.04, -0.19, 0.05, -0.08, 0, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.03, 0.1, 0.17, -0.03, 0.05, 0.01, 0.25, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.11, 0.15, 0.13, -0.11, 0.16, 0.05, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.05, 0.06, 0.1, 0.06, 0.18, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.09, 0.02, 0.3, 0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.02, 0.06, 0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.01, -0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN) 

cor_matrix1 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat <- melt(cor_matrix1[-52, ]) 


    r <- ggplot(data = dat, aes(x = Var1, y = Var2)) + 
    geom_tile(aes(fill = value), color = "white") + 
    scale_fill_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#770000", "red", "#ff8000", "#ffff00", "#ffffe5"))+ 
    theme(axis.title.x = element_blank(), 
    axis.title.y = element_blank(), 
    panel.background = element_blank())

樣品用於更正件矩陣代碼2：

cor_matrix2 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat <- melt(cor_matrix2[-52, ]) 
p <- ggplot(data = dat, aes(x = Var1, y = Var2)) + 
geom_tile(aes(fill = value), color = "white") + 
scale_fill_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#00007f", "#1212b2", "cyan", "#b4b4cc", "white"))

1) combine the _r_ and _p_ matrices so that _r_ shows up in the lower diagonal and _p_ in the upper diagonnal; 2) have the main diagonal to be a *black* line (a line that separates the two matrices); 3)certain values can be highlighted

來源

2016-01-22 Nameis

您將需要在繪圖之前合併數據，但對於同一件事物有多個圖例並不容易在ggplot中完成。此外，爲什麼當他們有不同的範圍/代表不同的實體時，他們需要進入同一個地塊？ – Heroka

他們代表來自2個不同人羣的數據，我想將它們並排放置以便於比較。我怎麼能有多個傳說？示例代碼非常感謝！ – Nameis

另外，如何獲得c值的黑色和固定（最小，最大）範圍對角線？以及如何突出顯示矩陣中的某些值（即黑色空心矩形）？ – Nameis

據我所知，ggplot不允許您在同一個繪圖中使用多個色階。它也使你的圖表更難以解釋。但是，你可以巧妙的搭配造型：

一些預處理來處理你的數據，我敢肯定你生成數據集時，你能避免一些這樣的：

cor_matrix1 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat1 <- melt(cor_matrix1[-52, ]) 
cor_matrix2 <- matrix(matrix_values, ncol = 51, nrow = 51) 
dat2 <- melt(cor_matrix2[-52, ]) 
dat2$Var1 <- 52 - dat2$Var1 
dat2$Var2 <- 52 - dat2$Var2 
dat1$Class <- "A" 
dat2$Class <- "B" 
dat <- rbind(dat1,dat2) 
dat <- dat[!is.nan(dat$value),]

而不是使用geom_tile，儘量geom_point。這會給你使用形狀的靈活性。（即一個額外的維度上進行細分數據）：

ggplot(data = dat, aes(x = Var1, y = Var2)) + 
    geom_point(size = 4, aes(color = value, pch = Class)) + 
    scale_color_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#00007f", "#1212b2", "cyan", "#b4b4cc", "white")) + 
    geom_abline(slope = -1,intercept = 52 , size = 2) + 
    geom_rect(xmin = 30, xmax = 31, ymin = 30, ymax = 31, color = "red", fill = NA) + 
    theme(axis.title.x = element_blank(), 
      axis.title.y = element_blank(), 
      panel.background = element_blank())

其中給出：

幾件事情會在這裏：

的pch參數geom_point爲每個人口設置不同的形狀
geom_abline給你沿着圖中心的黑線（你也可以用點來做這件事，但我認爲這是更清晰
geom_rect參數創建紅色矩形。根據需要調整分鐘數/最大值，並根據需要放入。

此外，請注意，這裏的負相關將變爲白色（按照您定義的顏色比例）。我會發現這種誤導。

來源

2016-01-22 22:26:54 Chris

R：如何兩個相關組合矩陣使用GGPLOT2

回答

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